Mechanical Engineering - Research Publications

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    Evaluating Rehabilitation Progress Using Motion Features Identified by Machine Learning
    Lu, L ; Tan, Y ; Klaic, M ; Galea, MP ; Khan, F ; Oliver, A ; Mareels, I ; Oetomo, D ; Zhao, E (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021-04)
    Evaluating progress throughout a patient's rehabilitation episode is critical for determining the effectiveness of the selected treatments and is an essential ingredient in personalised and evidence-based rehabilitation practice. The evaluation process is complex due to the inherently large human variations in motor recovery and the limitations of commonly used clinical measurement tools. Information recorded during a robot-assisted rehabilitation process can provide an effective means to continuously quantitatively assess movement performance and rehabilitation progress. However, selecting appropriate motion features for rehabilitation evaluation has always been challenging. This paper exploits unsupervised feature learning techniques to reduce the complexity of building the evaluation model of patients' progress. A new feature learning technique is developed to select the most significant features from a large amount of kinematic features measured from robotics, providing clinically useful information to health practitioners with reduction of modeling complexity. A novel indicator that uses monotonicity and trendability is proposed to evaluate kinematic features. The data used to develop the feature selection technique consist of kinematic data from robot-aided rehabilitation for a population of stroke patients. The selected kinematic features allow for human variations across a population of patients as well as over the sequence of rehabilitation sessions. The study is based on data records pertaining to 41 stroke patients using three different robot assisted exercises for upper limb rehabilitation. Consistent with the literature, the results indicate that features based on movement smoothness are the best measures among 17 kinematic features suitable to evaluate rehabilitation progress.
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    Effective Assessments of a Short-Duration Poor Posture on Upper Limb Muscle Fatigue Before Physical Exercise
    Lu, L ; Robinson, M ; Tan, Y ; Goonewardena, K ; Guo, X ; Mareels, I ; Oetomo, D (Frontiers Media, 2020-10-06)
    A forward head and rounded shoulder posture is a poor posture that is widely seen in everyday life. It is known that sitting in such a poor posture with long hours will bring health issues such as muscle pain. However, it is not known whether sitting in this poor posture for a short period of time will affect human activities. This paper investigates the effects of a short-duration poor posture before some typical physical activities such as push-ups. The experiments are set up as follows. Fourteen male subjects are asked to do push-ups until fatigue with two surface electromyography (sEMG) at the upper limb. Two days later, they are asked to sit in this poor posture for 15 min with eight sEMG sensors located at given back muscles. Then they do the push-ups after the short-duration poor posture. The observations from the median frequency of sEMG signals at the upper limb indicate that the short-duration poor posture does affect the fatigue procedure of push-ups. A significant decreasing trend of the performance of push-ups is obtained after sitting in this poor posture. Such effects indicate that some parts of the back muscles indeed get fatigued with only 15 min sitting in this poor posture. By further investigating the time-frequency components of sEMG of back muscles, it is observed that the low and middle frequencies of sEMG signals from the infraspinatus muscle of the dominant side are demonstrated to be more prone to fatigue with the poor posture. Although this study focuses only on push-ups, similar experiments can be arranged for other physical exercises as well. This study provides new insights into the effect of a short-duration poor posture before physical activities. These insights can be used to guide athletes to pay attention to postures before physical activities to improve performance and reduce the risk of injury.
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    Extremum Seeking Control with Sporadic Packet Transmission for Networked Control Systems
    Premaratne, U ; Halgamuge, S ; Tan, Y ; Mareels, IMY (IEEE, 2020-06)
    Extremum Seeking Control (ESC) is a data-driven optimization technique that can steer a dynamic plant towards an extremum of an unknown but measurable, input to steady-state map. In the context of Networked Control Systems (NCS) a new implementation method for ESC inspired by the well known Luus-Jaakola algorithm is proposed. The main motivation is to minimize the communication burden associated with the search phase of ESC. In the proposed method the controller only requires a notification of a change registered at the sensor, rather than the full information available at the sensor. This event based approach leads to sporadic packet transmission. In addition the proposed method is able to directly account for constraints whilst seeking for the desired extremum. The constraints may be of the inequality or equality type. The algorithm's behavior is illustrated on a networked water pump control system.